Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network

Traffic simulators can capture the complex dynamics of road networks thus are widely used to understand the behavior of the traffic system and predict the effects of transportation technologies, events and new policy changes. To build a traffic simulation model that can accurately capture the real traffic conditions, the Origin-Destination (OD) matrix is needed to be fed into the simulator as an input. In general, it is difficult to acquire information about the real OD matrix of a network. In this paper, we propose a neural network model for estimating the OD matrix that excites the microscopic simulation model for complex traffic networks to reproduce real world traffic flows using only the flow rate information of a small subset of links in the traffic network, which is relatively easy to collect. An optimization method is used to generate the training dataset of the neural network. Combining the OD matrix estimation model, the training dataset generation method, and the microscopic traffic simulator, an integrated traffic simulation-prediction system is developed to predict the behavior of the transportation system. We test the proposed system on the road network of the central terminal area of the Los Angeles International Airport and demonstrate that the integrated traffic simulation-prediction system can reproduce the real world traffic flow with low relative root mean square errors especially for high volume links and can be used to simulate the behavior of real world traffic scenarios.